The next experiments evaluate the influence of FL hyperparameters on AED performance, namely the total number of clients, \(N\) , the percentage of total clients that participate in each training round (participation rate \(r_p\) ), and number of local epochs performed during each round, \(E_L\) . For \(N\)  we evaluate 1, 25, and 100 clients, with \(N=1\)  providing a FL baseline. For \(r_p\) , values of \(\lbrace 0.25, 0.5, 1.0\rbrace \)  are used in order to simulate the uncertainty of client participation during each communication round. At each round a fraction, \(r_p\) , of the \(N\)  clients are uniformly sampled (with replacement at the end of each round) to participate in training.
As the amount of network communication should be limited in FL, we evaluate the effects of \(E_L=\lbrace 1, 3, 5\rbrace \)  to reduce the number of communication rounds by increasing amount of training steps performed on each client before sending weight updates. For all FL experiments, the local client models are optimized using Adam, with a learning rate \(lr=1e-04\) , proposed by Leroy et al. [1]}. The local weight updates are aggregated by the coordinator using the standard Federating Averaging algorithm using SGD with a learning rate of \(1.0\)  [2]}.
